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Versatility - find how closely a node in a graph is associated with a community

Project description

# Versatility

This package implements versatility [(Shinn et al., 2017)](https://www.nature.com/articles/s41598-017-03394-5), which describes how closely affiliated a node is with a network community structure. It is written in Python3, and can only be guaranteed to work there. (This MAY work in Python2 if you import __future__ but this is untested… see code for details.)

Install with:

pip3 install versatility

Alternatively, clone the git repo and install with:

python3 setup.py install

Dependencies:

See function help for full documentation, but the most useful functions are:

  • find_nodal_versatility - Compute the versatility of each node in a graph using a specific community detection algorithm.

  • find_nodal_mean_versatility - Compute the versatility of each node across a spectrum of community detection algorithm parameters (most notably the resolution parameter) and find the average.

  • find_optimal_gamma_curve - Find the mean and standard error of versatility across a spectrum of resolution parameters and (optionally) plot the result. This is most useful for finding the best resolution parameter, e.g. in [Figure 3c of the original paper](https://www.nature.com/articles/s41598-017-03394-5/figures/3).

Here is a quick example to get you started:

import networkx from versatility import * G = networkx.karate_club_graph() find_nodal_mean_versatility(G, find_communities_louvain, processors=2) find_nodal_versatility(G, find_communities_louvain, algargs={“gamma” : 0.5})

If you use this code, please cite:

Shinn, M., Romero-Garcia, R., Seidlitz, J., Vasa, F., Vertes, P., Bullmore, E. (2017). Versatility of nodal affiliation to communities. Scientific Reports 7: 4273. doi:10.1038/s41598-017-03394-5

Copyright 2016-2019 Maxwell Shinn (maxwell.shinn@yale.edu) Available under the GNU GPLv3.

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